Instructions to use weifeng-chen/controlavideo-hed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use weifeng-chen/controlavideo-hed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("weifeng-chen/controlavideo-hed", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download controlnet/diffusion_pytorch_model.bin from weifeng-chen/controlavideo-hed: direct link, hf CLI and curl.
- Browser
- Download file 1.32 GB
-
https://huggingface.co/weifeng-chen/controlavideo-hed/resolve/main/controlnet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://weifeng-chen/controlavideo-hed/controlnet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/weifeng-chen/controlavideo-hed/resolve/main/controlnet/diffusion_pytorch_model.bin
1.32 GB
- Xet hash:
- 61f6599aa5e73c149a0f193f53b96458ab7e10e08aa60f5aef41c9be147eb2ff
- Size of remote file:
- 1.32 GB
- SHA256:
- a089692bb820852baf8990107e1fb6893530f0f54750d395a93ceb866d0658bb
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